Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force
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Luciano Minuzzi | Benson Mwangi | Tomas Hajek | Rodrigo C. Barros | Martin Alda | Ives C. Passos | Pedro L. Ballester | Diego Librenza‐Garcia | Boris Birmaher | Elisa Brietzke | Carlos Lopez Jaramillo | Rodrigo B. Mansur | Bartholomeus C. M. Haarman | Erkki Isometsa | Raymond W. Lam | Roger S. McIntyre | Lars V. Kessing | Lakshmi N. Yatham | Anne Duffy | Flavio Kapczinski | M. Alda | B. Mwangi | R. Lam | R. McIntyre | L. Kessing | L. Minuzzi | T. Hajek | A. Duffy | I. Passos | E. Brietzke | F. Kapczinski | R. Mansur | B. Birmaher | Rodrigo C. Barros | L. Yatham | D. Librenza-Garcia | C. L. López Jaramillo | B. Haarman | P. Ballester | E. Isometsa
[1] B. O’Donnell,et al. Diagnostic specificity of neurophysiological endophenotypes in schizophrenia and bipolar disorder. , 2013, Schizophrenia bulletin.
[2] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[3] P. Grof,et al. The Emergent Course of Bipolar Disorder: Observations Over Two Decades From the Canadian High-Risk Offspring Cohort. , 2019, The American journal of psychiatry.
[4] David A. Ross,et al. Computational Psychiatry: Embracing Uncertainty and Focusing on Individuals, Not Averages , 2017, Biological Psychiatry.
[5] P. Andersen,et al. Risk of recurrence after a single manic or mixed episode – a systematic review and meta‐analysis , 2018, Bipolar disorders.
[6] A. Chekroud,et al. Bigger Data, Harder Questions-Opportunities Throughout Mental Health Care. , 2017, JAMA psychiatry.
[7] E. Bramon,et al. Patterns of deficits in brain function in bipolar disorder and schizophrenia: A cluster analytic study , 2012, Psychiatry Research.
[8] M. Vinberg,et al. A multisystem composite biomarker as a preliminary diagnostic test in bipolar disorder , 2018, Acta psychiatrica Scandinavica.
[9] Ives Cavalcante Passos,et al. Peripheral biomarker signatures of bipolar disorder and schizophrenia: A machine learning approach , 2017, Schizophrenia Research.
[10] M. Alda,et al. The implications of genetics studies of major mood disorders for clinical practice. , 2000, The Journal of clinical psychiatry.
[11] C. Beckmann,et al. Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[12] A. Duffy,et al. Changing the bipolar illness trajectory. , 2017, The lancet. Psychiatry.
[13] Jan Struyf,et al. Combining gene expression, demographic and clinical data in modeling disease: a case study of bipolar disorder and schizophrenia , 2008, BMC Genomics.
[14] Ruiwang Huang,et al. Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression , 2018, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[15] S. Schulz,et al. Assessment of Proteomic Measures Across Serious Psychiatric Illness. , 2017, Clinical schizophrenia & related psychoses.
[16] M. Vinberg,et al. A composite peripheral blood gene expression measure as a potential diagnostic biomarker in bipolar disorder , 2015, Translational Psychiatry.
[17] P. Grof,et al. The developmental trajectory of bipolar disorder. , 2014, The British journal of psychiatry : the journal of mental science.
[18] Chung-Hsien Wu,et al. Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio–visual signals , 2016, J. Ambient Intell. Humaniz. Comput..
[19] Husamettin Gul,et al. Evaluation of potential novel variations and their interactions related to bipolar disorders: analysis of genome-wide association study data , 2016, Neuropsychiatric disease and treatment.
[20] Ahmad Khodayari-Rostamabad,et al. Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[21] P. Mitchell,et al. ECNP consensus meeting. Bipolar depression. Nice, March 2007 , 2008, European Neuropsychopharmacology.
[22] Jeremy Ginsberg,et al. Detecting influenza epidemics using search engine query data , 2009, Nature.
[23] Bernard Marr,et al. Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance , 2015 .
[24] Khader M Hasan,et al. Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[25] T. Greenhalgh,et al. Evidence based medicine: a movement in crisis? , 2014, BMJ : British Medical Journal.
[26] J. Bardram,et al. Voice analysis as an objective state marker in bipolar disorder , 2016, Translational psychiatry.
[27] P. McSharry,et al. Lamotrigine Therapy for Bipolar Depression: Analysis of Self-Reported Patient Data , 2018, JMIR Mental Health.
[28] F. Gage,et al. Neurons derived from patients with bipolar disorder divide into intrinsically different sub-populations of neurons, predicting the patients’ responsiveness to lithium , 2018, Molecular Psychiatry.
[29] M. Phillips,et al. Brain morphometric biomarkers distinguishing unipolar and bipolar depression. A voxel-based morphometry-pattern classification approach. , 2014, JAMA psychiatry.
[30] B. Mwangi,et al. The impact of machine learning techniques in the study of bipolar disorder: A systematic review , 2017, Neuroscience & Biobehavioral Reviews.
[31] Fatemeh Alimardani,et al. Classification of Bipolar Disorder and Schizophrenia Using Steady-State Visual Evoked Potential Based Features , 2018, IEEE Access.
[32] R. Yolken,et al. Towards a blood-based diagnostic panel for bipolar disorder , 2016, Brain, Behavior, and Immunity.
[33] O. Mayora,et al. The Bipolar Illness Onset study: research protocol for the BIO cohort study , 2017, BMJ Open.
[34] Turker Tekin Erguzel,et al. Artificial intelligence approach to classify unipolar and bipolar depressive disorders , 2015, Neural Computing and Applications.
[35] Tomas Hajek,et al. Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. , 2015, Journal of psychiatry & neuroscience : JPN.
[36] Stephen Wilson. Data protection: Big data held to privacy laws, too , 2015, Nature.
[37] D. Lazer,et al. The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.
[38] Xiaolin Li,et al. DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning , 2017, Human mutation.
[39] P. Grof,et al. Attachment and temperament profiles among the offspring of a parent with bipolar disorder. , 2013, Journal of affective disorders.
[40] H. Lôo. [On bipolar disorder]. , 2008, L'Encephale.
[41] Shantanu H. Joshi,et al. Methylphenidate modifies the motion of the circadian clock Lamotrigine in mood disorders and cocaine dependence Cortical glutamate in postpartum depression Effect of Electroconvulsive Therapy on Striatal Morphometry in Major Depressive Disorder , 2016 .
[42] Ryan M. Cassidy,et al. Neuroprogression and illness trajectories in bipolar disorder , 2017, Expert review of neurotherapeutics.
[43] Bradley E. Belsher,et al. Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation. , 2019, JAMA psychiatry.
[44] Tung Tran,et al. Predicting mental conditions based on "history of present illness" in psychiatric notes with deep neural networks. , 2017, Journal of biomedical informatics.
[45] Chung-Hsien Wu,et al. Mood disorder identification using deep bottleneck features of elicited speech , 2017, 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).
[46] Ives Cavalcante Passos,et al. Big data analytics and machine learning: 2015 and beyond. , 2016, The lancet. Psychiatry.
[47] Khader M. Hasan,et al. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning , 2017, NeuroImage.
[48] P. Fitzgerald,et al. Bipolar disorder in the balance , 2018, European Archives of Psychiatry and Clinical Neuroscience.
[49] I. Passos,et al. Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf’s personal writings , 2018, PloS one.
[50] Timothy C.Y. Chan,et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. , 2018, Journal of affective disorders.
[51] J. Soares,et al. Predictors of psychiatric readmission among patients with bipolar disorder at an academic safety-net hospital , 2016, The Australian and New Zealand journal of psychiatry.
[52] B. Mwangi,et al. Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. , 2016, Journal of affective disorders.
[53] Benson Mwangi,et al. A Review of Feature Reduction Techniques in Neuroimaging , 2013, Neuroinformatics.
[54] R. Hirschfeld,et al. The National Depressive and Manic-depressive Association (DMDA) survey of bipolar members. , 1994, Journal of affective disorders.
[55] J. Ioannidis,et al. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration , 2009, Annals of Internal Medicine [serial online].
[56] Terry Lyons,et al. A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder , 2017, Translational Psychiatry.
[57] C. Hammen,et al. Relapse and impairment in bipolar disorder. , 1995, The American journal of psychiatry.
[58] Turker Tekin Erguzel,et al. A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders , 2015, Comput. Biol. Medicine.
[59] J. Geddes,et al. Daily and weekly mood ratings using a remote capture method in high‐risk offspring of bipolar parents: Compliance and symptom monitoring , 2018, Bipolar disorders.
[60] J. Soares,et al. Refractory bipolar disorder and neuroprogression , 2016, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[61] J. Calabrese,et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) 2018 guidelines for the management of patients with bipolar disorder , 2018, Bipolar disorders.
[62] Neil Savage,et al. Machine learning: Calculating disease , 2017, Nature.
[63] N. Koutsouleris,et al. The perilous path from publication to practice , 2018, Molecular Psychiatry.
[64] Jun Liu,et al. Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses , 2012, PloS one.
[65] Klaus P. Ebmeier,et al. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. , 2012, Brain : a journal of neurology.
[66] J. Rosenbaum,et al. Stratifying Risk for Renal Insufficiency Among Lithium-Treated Patients: An Electronic Health Record Study , 2016, Neuropsychopharmacology.
[67] A. Young,et al. A randomised, placebo-controlled 52-week trial of continued quetiapine treatment in recently depressed patients with bipolar I and bipolar II disorder , 2014, The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry.
[68] Z. Obermeyer,et al. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.
[69] Dinh Q. Phung,et al. Using linguistic and topic analysis to classify sub-groups of online depression communities , 2015, Multimedia Tools and Applications.
[70] S. Strakowski,et al. Prediction of lithium response in first‐episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof‐of‐concept , 2017, Bipolar disorders.
[71] P. Andersen,et al. Evidence for clinical progression of unipolar and bipolar disorders , 2017, Acta psychiatrica Scandinavica.
[72] B. Mwangi,et al. Machine learning-guided intervention trials to predict treatment response at an individual patient level: an important second step following randomized clinical trials , 2018, Molecular Psychiatry.
[73] R. Kessler,et al. Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. , 2007, Archives of general psychiatry.
[74] P. Grof,et al. Early exposure to parental bipolar disorder and risk of mood disorder: the Flourish Canadian prospective offspring cohort study , 2015, Early intervention in psychiatry.
[75] F. Kapczinski,et al. Childhood trauma, family history, and their association with mood disorders in early adulthood , 2016, Acta psychiatrica Scandinavica.
[76] B. Mwangi,et al. Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning. , 2016, Journal of affective disorders.
[77] D. Kupfer,et al. Toward the Definition of a Bipolar Prodrome: Dimensional Predictors of Bipolar Spectrum Disorders in At-Risk Youths. , 2016, The American journal of psychiatry.
[78] N. Ryan,et al. A Risk Calculator to Predict the Individual Risk of Conversion From Subthreshold Bipolar Symptoms to Bipolar Disorder I or II in Youth. , 2018, Journal of the American Academy of Child and Adolescent Psychiatry.
[79] Patrick C. Staples,et al. Relapse prediction in schizophrenia through digital phenotyping: a pilot study , 2018, Neuropsychopharmacology.
[80] M. Alda,et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) collaborative update of CANMAT guidelines for the management of patients with bipolar disorder: update 2013. , 2013, Bipolar disorders.
[81] M. Alda,et al. The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases , 2013, PloS one.
[82] Preben Bo Mortensen,et al. Absolute risk of suicide after first hospital contact in mental disorder. , 2011, Archives of general psychiatry.
[83] B. Mwangi,et al. Areas of controversy in neuroprogression in bipolar disorder , 2016, Acta psychiatrica Scandinavica.
[84] Stephen Begg,et al. Adjusting for dependent comorbidity in the calculation of healthy life expectancy , 2006, Population health metrics.
[85] J. Calabrese,et al. Lamotrigine for treatment of bipolar depression: independent meta-analysis and meta-regression of individual patient data from five randomised trials , 2009, British Journal of Psychiatry.
[86] A. Simmons,et al. Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach , 2012, Psychological Medicine.
[87] N. Schork,et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach , 2015, Molecular Psychiatry.
[88] M. Preisig,et al. The association between self-reported and clinically determined hypomanic symptoms and the onset of major mood disorders , 2017, BJPsych open.
[89] N. Schork,et al. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment , 2016, Molecular Psychiatry.
[90] M. Alda,et al. Lithium response across generations , 2009, Acta psychiatrica Scandinavica.
[91] Inês de Castro Dutra,et al. A Multi-Relational Model for Depression Relapse in Patients with Bipolar Disorder , 2015, MedInfo.
[92] J. Ioannidis,et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration , 2009, BMJ : British Medical Journal.
[93] J. Sääf,et al. Affective disorder subtyped by psychomotor symptoms, monoamine oxidase, melatonin and cortisol: identification of patients with latent bipolar disorder , 1998, European Archives of Psychiatry and Clinical Neuroscience.
[94] Manuel Graña,et al. Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. , 2015 .
[95] J. Hauser,et al. Clock gene variants differentiate mood disorders , 2014, Molecular Biology Reports.
[96] Po-Hsiu Kuo,et al. Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm , 2017, Scientific Reports.
[97] Erick Jorge Canales-Rodríguez,et al. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group , 2018, Molecular Psychiatry.
[98] M. Alda,et al. Prophylactic treatment response in bipolar disorder: results of a naturalistic observation study. , 2007, Journal of affective disorders.
[99] VenkateshSvetha,et al. Using linguistic and topic analysis to classify sub-groups of online depression communities , 2017 .
[100] Mehdi Pirooznia,et al. Data mining approaches for genome-wide association of mood disorders , 2012, BIOCOMP.